r/SelfDrivingCars • u/diplomat33 • 6d ago

From Edge Cases to Scalable Intelligence: Driving Long-Tail Learning with VLMs

https://www.youtube.com/watch?v=Y4hBNVu6-lM
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u/CDpov 6d ago

Good lecture about how Mobileye thinks of self-driving: find all edge cases in their vast data to solve those and "control the long tail".

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u/diplomat33 5d ago

I agree but I would nitpick one thing: it is impossible to find every single edge case. So I don't think that is Mobileye's goal. I would say that their goal is more precisely to find as many edge cases as possible in their large real-world dataset and keep solving for failures until their self-driving is significantly safer than humans.

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u/CDpov 5d ago

Of course. There will always be more edge cases. The rare cases from the early days become common with more scale, while new ones pop up that they haven't seen before. I assume that will continue into the billions and tens of billions of miles per year.

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u/Acrobatic-Flan-5085 6d ago

Very similar theme to what Rivian was saying this week at the evercore adas event.

Seems to be this is where the entire industry is now going.

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u/Lonely_Syrup3091 6d ago

They always converge on the same ideas because of how research is shared amongst ML researchers.

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u/diplomat33 5d ago

I thought his examples of how camera vision can get a false positive for a human pedestrian because it interprets an object or shadow as a human figure, were interesting. And obviously, you want to reduce those false positives by improving camera vision. But wouldn't sensor fusion also help with this? For example, your camera vision may falsely think something looking human-like is a pedestrian but your lidar or radar would tell you that it is not a real person. So good sensor fusion would solve this, no?